Early detection of current hot spots in power gated designs
Bibliographic record
Abstract
Abstract—With the growing popularity of hand-held batterypowered devices, leakage power is a major concern in the nanometer CMOS era. Power gating technique is an effective and widely adopted solution to this problem. The challenge of implementing power gating is the sizing and placement of the sleep transistors that are used to gate the power supply. In a placed design, due to non-uniform current demand of logic cells, some regions of the chip can have sleep transistors with very high current demand, causing power grid noise violations. Identifying these regions early in the design cycle is critical to the success of power gating implementation. This paper presents a novel methodology to calculate the current demand of each sleep transistor and locate regions in the chip where multiple sleep transistors experience very high current demand. In this paper, we model the spatial locality of the current drawn by each logic cells in the form of a bounding box. We explore techniques to identify the appropriate size of the bounding boxes. Furthermore, we extend the current distribution technique to handle placement blockages that do not share the sleep transistor network of the chip. Experimental results on industrial circuits show that the proposed algorithm can identify over 90 % of such regions with a 20x run-time reduction compared to state-of-the-art commercial CAD tool.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".